9781789616729-1789616727-Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition

Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition

ISBN-13: 9781789616729
ISBN-10: 1789616727
Author: Liu, Yuxi (Hayden)
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 382 pages
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Book details

ISBN-13: 9781789616729
ISBN-10: 1789616727
Author: Liu, Yuxi (Hayden)
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 382 pages

Summary

Acknowledged authors Liu, Yuxi (Hayden) wrote Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition comprising 382 pages back in 2019. Textbook and eTextbook are published under ISBN 1789616727 and 9781789616729. Since then Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn

Key Features
  • Exploit the power of Python to explore the world of data mining and data analytics
  • Discover machine learning algorithms to solve complex challenges faced by data scientists today
  • Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects
Book Description A surging interest in machine learning is due to the fact that it evolutionzies automation by learning patterns in data and using them to make predictions and decisions. Your ML journey starts with this book, as the second edition of the bestseller, Python Machine Learning By Example.
Hayden's unique insights and expertise introduce you to important ML concepts and implementations of algorithms in Python both from scratch and with libraries. Each chapter of the book walks you through an industry adopted application. With the help of realistic examples, you will find it intriguing to acquire mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP - they are no more obscure as you thought.
This critically extended and updated edition now includes implementation with trendy libraries including TensorFlow, gensim and Keras. The scikit-learn codes are also fully modernized. Even if you've read the last edition, you'll still be delighted to find plenty of new content, for example, neural network, dimensionality reduction, topic modeling, large-scale learning with Spark and word embedding.
Toward the end, you will gather a broad picture of the ML ecosystem and best practices of applying ML techniques to meet new opportunities in today's world.
What you will learn
  • Understand the important concepts in machine learning and data science
  • Use Python to explore the world of data mining and analytics
  • Scale up model training using varied data complexities with Apache Spark
  • Delve deep into text and NLP using Python libraries such NLTK and gensim
  • Select and build an ML model and evaluate and optimize its performance
  • Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn
Who this book is for If you're a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
Table of Contents
  1. Getting Started with Machine Learning and Python
  2. Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
  3. Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
  4. Detecting Spam Email with Naive Bayes
  5. Classifying News Topic with Support Vector Machine
  6. Predicting Online Ads Click-through with Tree-Based Algorithms
  7. Predicting Online Ads Click-through with Logistic Regression
  8. Scaling Up Prediction to Terabyte Click Logs
  9. Stock Price Prediction with Regression Algorithms
  10. Machine Learning Best Practices
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